Bayesian Methods for Hackers An intro to Bayesian methods and probabilistic programming from a computation/understanding-first, mathematics-second point of view. Prologue The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. After some recent success of Bayesian methods in machine-learning competitions, I decided to investigate the subject again. If Bayesian inference is the destination, then mathematical analysis is a particular path towards it. Bayesian Methods for Hackers is designed as a introduction to Bayesian inference from a computational/understanding-first, and mathematics-second, point of view. The choice of PyMC as the probabilistic programming language is two-fold. PyMC does have dependencies to run, namely NumPy and (optionally) SciPy. Printed version now available! Bayesian Methods for Hackers is now available in print. Differences between the print version and the online version include: Contents Examples from the book
District Data Labs - Modern Methods for Sentiment Analysis Modern Methods for Sentiment Analysis Michael Czerny Sentiment analysis is a common application of Natural Language Processing (NLP) methodologies, particularly classification, whose goal is to extract the emotional content in text. The simplest form of sentiment analysis is to use a dictionary of good and bad words. Another common method is to treat a text as a “bag of words”. Word2Vec and Doc2Vec Recently, Google developed a method called Word2Vec that captures the context of words, while at the same time reducing the size of the data. Figure 1: Architecture for the CBOW and Skip-gram method, taken from Efficient Estimation of Word Representations in Vector Space. These word vectors now capture the context of surrounding words. However, even with the above method of averaging word vectors, we are ignoring word order. Figure 2: Architecture for Doc2Vec, taken from Distributed Representations of Sentences and Documents. Word2Vec Example in Python "ate" - "eat" + "speak" = "spoke" Conclusion
Former Facebook executive says society will COLLAPSE within 30 years as robots put half of humans out of work A former Facebook executive has quit his job and now lives as a recluse in the wilderness - because he is convinced that machines will take over the world. Antonio Garcia Martinez worked as a project manager for the social media giant in Silicon Valley but became terrified by the relentless march of technology. Facebook Getty - Contributor He reckons that machines will have taken half of humanity's jobs within 30 years, sparking revolt and armed conflict. So he quit his job, fled his home and now lives in woodland north of Seattle with a gun for protection. He spoke to new two-part BBC2 documentary "Secrets of Silicon Valley", which explores the growing influence of the tech hub on global development. Mr Martinez said: "If the world really does end, there aren't going to be many places to run. "Within 30 years, half of humanity won't have a job. "I've seen what the world will look like in five to 10 years. This video isn't encoded for your device Getty Images Facebook friend
Understanding Convolutional Neural Networks for NLP | WildML When we hear about Convolutional Neural Network (CNNs), we typically think of Computer Vision. CNNs were responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today, from Facebook’s automated photo tagging to self-driving cars. More recently we’ve also started to apply CNNs to problems in Natural Language Processing and gotten some interesting results. In this post I’ll try to summarize what CNNs are, and how they’re used in NLP. The intuitions behind CNNs are somewhat easier to understand for the Computer Vision use case, so I’ll start there, and then slowly move towards NLP. What is Convolution? The for me easiest way to understand a convolution is by thinking of it as a sliding window function applied to a matrix. Convolution with 3×3 Filter. Imagine that the matrix on the left represents an black and white image. You may be wondering wonder what you can actually do with this. The GIMP manual has a few other examples. Narrow vs. .
How Ideology Is Like Pokemon Go Released in July 2016, Pokémon Go is a location-based, augmented-reality game for mobile devices, typically played on mobile phones; players use the device’s GPS and camera to capture, battle, and train virtual creatures (“Pokémon”) who appear on the screen as if they were in the same real-world location as the player: As players travel the real world, their avatar moves along the game’s map. Different Pokémon species reside in different areas—for example, water-type Pokémon are generally found near water. When a player encounters a Pokémon, AR (Augmented Reality) mode uses the camera and gyroscope on the player’s mobile device to display an image of a Pokémon as though it were in the real world. The first step in this direction of technology imitating ideology was taken a couple of years ago by Pranav Mistry, a member of the Fluid Interfaces Group at the Massachusetts Institute of Technology Media Lab, who developed a wearable “gestural interface” called “SixthSense.” References 1. 2.
Understanding Natural Language with Deep Neural Networks Using Torch This post was co-written by Soumith Chintala and Wojciech Zaremba of Facebook AI Research. Language is the medium of human communication. Giving machines the ability to learn and understand language enables products and possibilities that are not imaginable today. One can understand language at varying granularities. As an example, language understanding gives one the ability to understand that the sentences “I’m on my way home.” and “I’m driving back home.” both convey that the speaker is going home. Word Maps and Language Models For a machine to understand language, it first has to develop a mental map of words, their meanings and interactions with other words. Word embeddings can either be learned in a general-purpose fashion before-hand by reading large amounts of text (like Wikipedia), or specially learned for a particular task (like sentiment analysis). An even simpler metric is to predict the next word in the sentence. “I am eating _____” “I am eating an apple.” Learn More at GTC 2015
Take it from the insiders: Silicon Valley is eating your soul | John Harris One source of angst came close to being 2017’s signature subject: how the internet and the tiny handful of companies that dominate it are affecting both individual minds and the present and future of the planet. The old idea of the online world as a burgeoning utopia looks to have peaked around the time of the Arab spring, and is in retreat. If you want a sense of how much has changed, picture the president of the US tweeting his latest provocation in the small hours, and consider an array of words and phrases now freighted with meaning: Russia, bots, troll farms, online abuse, fake news, dark money. Another sign of how much things have shifted is a volte-face by Silicon Valley’s most powerful man. The company has reached a fascinating point in its evolution; it is as replete with importance and interest as any political party. Then there is Tristan Harris, a former high-up at Google who is now hailed as “the closest thing Silicon Valley has to a conscience”. Good for him.
NaturalNode/natural The Case for Responsible Innovation | DigitalNext Is technological innovation good or bad? Seems like a silly question on the surface. But we have questions: Can self-driving cars ever be safe? We have concerns: Fake news, fake ads, fake accounts, bots, foreign governments interfering with our elections … The courts have historically decided that technology is neither intrinsically good nor bad, but they have expressed the opinion that people must be responsible and held accountable for how it is used. It's illegal to text and drive. Fear, uncertainty and doubt in Las Vegas Once a beacon of optimism, the tech industry has come under pressure as concerns mount about potential negative impacts of innovation. My colleagues at PwC and I agree that the time has come to seriously consider a responsible approach to innovation. At CES in Las Vegas next week, we'll present a discussion that explores the three basic approaches to the problem of regulating technological innovation: 1. 2. 3. Our thinking
unrealengine Jan 3, 2018 Unreal Engine 4 Mastery: Create Multiplayer Games with C++ in New Course from Udemy By Daniel Kayser Unleash the power of C++ and Blueprint to develop Multiplayer Games with AI... Jan 1, 2018 Getting Started with Unreal Multiplayer in C++ By Sam Pattuzzi While Unreal Engine offers fantastic multiplayer support right out of the b... Dec 30, 2017 Unreal Engine Developers Featured in IndieDB’s Top 100 of 2017 By Jess Hider As the year comes to a close, it’s a perfect time to look back on some of t... 15 Myths That Can Ruin Your Mobile UX Mobile usage is steadily rising. The number of mobile phone users in the world will pass the five billion mark by 2019, and more than ever, people are engaging with their phones in crucial moments. The average U.S. user spends five hours per day on mobile, and the vast majority of that time is spent on apps and websites—making mobile an extremely valuable medium for app developers. What makes a good app and what makes a bad app? The difference often comes down to the quality of its user experience, which is why product teams spend countless hours perfecting their mobile UX. But like many disciplines, mobile design suffers from misconceptions and myths that can prevent designers from creating efficient products. In this article, we’ll look at 15 common mobile misperceptions, and dispel them with a dose of reality. You might also like: 5 of the Best Prototyping Tools to Test Out Your Web and Mobile Designs. Myth 1: Mobile users are always on the run Myth 2: The best designs are invisible
These Are The Technologies That Will Change Our Lives In The Next 10 Years Fast charging will help electric cars such as the Mercedes-Benz EQ Silver Arrow electric concept vehicle. Photographer: David Paul Morris/Bloomberg© 2018 Bloomberg Finance LP We are living in an age of massive technological change, but sometimes it is all so complicated it can be hard to work out what’s really going to change the world and what will fall by the wayside. Researchers at Lux have looked at the key technology innovations that are going to change the world economy – and our lives over the next 10 years. Its 19 for 2019 report looks at the innovations that are facing market roadblocks and those that are more likely to succeed because they fit an unmet market need. Battery fast-charging is one of a number of clean energy technologies on the list and will be a key enabler for the growth of the electric vehicle market, while solid state batteries will help to boost range in EVs and could move the industry beyond the lithium-ion era.
Clean up your cyber-hygiene – 6 changes to make in the new year Data breaches, widespread malware attacks and microtargeted personalized advertising were lowlights of digital life in 2018. As technologies change, so does the advice security experts give for how to best stay safe. As 2019 begins, I’ve pulled together a short list of suggestions for keeping your digital life secure and free of manipulative disinformation. 1. Set your boundaries and stick to them As part of my research, I’ve recently been speaking with a number of sex workers in Europe about their digital security and privacy. That way, when the latest new app asks you for a permission that oversteps what you’re willing to share, you’ll be more prepared to answer. 2. People who get their news primarily – or exclusively – from social media are subjecting themselves to the whims of the algorithms that decide what to display to each user. Because of how these algorithms work, those people are likely to see articles only from news sources they already like and tend to agree with. 3. 4. 5. 6.